Impact of Artificial Intelligence & Cognitive Automation on Next-gen Digital Workforce Platforms
THIS ARTICLE WAS ORIGINALLY PUBLISHED ON AUTOMATION ANYWHERE ON NOV 21, 2018
The world has gone digital and automation has become a critical success factor. Across all industries, enterprises leverage Robotic Process Automation (RPA) to automate business processes such as insurance claims processing, invoice processing, employee onboarding, support center, account reconciliation, patient outreach and many others.
By Avi Bhagtani
The world has gone digital and automation has become a critical success factor. Across all industries, enterprises leverage Robotic Process Automation (RPA) to automate business processes such as insurance claims processing, invoice processing, employee onboarding, support center, account reconciliation, patient outreach and many others. These automated services allow companies to offer their users 24x7 experience with unprecedented levels of accuracy and reliability.
To keep up with the business and customer needs, automation must continue to evolve to include a new class of products that “reason and learn ”. A new era of digital transformation is led by a Digital Workforce that combines Artificial Intelligence (AI) and cognitive automation, mixed with the right amount of human interaction. This next generation of automation addresses specific challenges in each vertical and industry.
Understanding when the combination of AI—such as machine learning, Natural Language Processing (NLP), cognition, data modeling—and RPA is beneficial will be critical to the success of any company that is undergoing a digital transformation. A few examples include:
Data Synthesis: Today, data comes from multiple sources and in structured or unstructured formats. Using AI to extract critical data and feeding it to RPA is a critical step in this process to ensure a smooth flow of accurate information.
Understanding Context: An important step in a full cycle automation is to understand the right instructions or intent. The process of applying cognition here is to assist RPA in determining the right next steps based on what a user is trying to accomplish. Leveraging NLP to “read” and “understand” information in an email can help with the routing or process selection.
Predictive Modeling: Apart from extracting data and understanding intent, AI can be extended to make predictions and outcomes. Using machine learning (ML) at the right time during process automation allows decision modeling to recommend the best course of actions. Banks, for example, can use ML models to predict the occurrence of fraud.
According to IDC, the worldwide spend for cognitive/Artificial Intelligence (AI) systems will exceed $57 billion at a compound annual growth rate of 50.1% through 2021. As the implementation of AI and cognitive automation continues to grow, business must determine how to apply cognitive automation to improve adoption and yield ROI.
Read our upcoming blogs to explore how this new breed of the intelligent Digital Workforce is impacting industries and learn how you can be at the forefront of true digital business transformation.